{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple example of clustering with 2 attributes using SciKit Learn's k-Means and GMM (Gaussian Mixture Models) algorithms.\n",
    "\n",
    "## Both models will be exported into ONNX format with the proper metadata.json required by OML Services for scoring\n",
    "\n",
    "Copyright (c) 2021 Oracle Corporation                             \n",
    "###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import skl2onnx\n",
    "from skl2onnx import to_onnx\n",
    "\n",
    "import onnxmltools\n",
    "import json\n",
    "\n",
    "from zipfile import ZipFile\n",
    "\n",
    "# Loads the Breast Cancer dataset\n",
    "bc=datasets.load_breast_cancer(as_frame=True)\n",
    "# Creates a Pandas Dataframe\n",
    "df = pd.DataFrame(bc.data, columns = bc.feature_names)\n",
    "\n",
    "#Check the whole input dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Original Data Scatter Plot')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot two of the attributes to see the dispersion patterns\n",
    "\n",
    "# Set a nicer looking chart style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Generate a Scatterplot of two attributes\n",
    "plt.scatter(df['worst radius'], df['worst concavity'], cmap='plasma', edgecolor='k')\n",
    "plt.xlabel(\"Worst Radius\")\n",
    "plt.ylabel(\"Worst Concavity\")\n",
    "plt.title(\"Original Data Scatter Plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst concavity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.38</td>\n",
       "      <td>0.7119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.99</td>\n",
       "      <td>0.2416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.57</td>\n",
       "      <td>0.4504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.91</td>\n",
       "      <td>0.6869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.54</td>\n",
       "      <td>0.4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   worst radius  worst concavity\n",
       "0         25.38           0.7119\n",
       "1         24.99           0.2416\n",
       "2         23.57           0.4504\n",
       "3         14.91           0.6869\n",
       "4         22.54           0.4000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create a subset with only the 2 attributes\n",
    "df_sub = df[['worst radius','worst concavity']]\n",
    "# Check the first 5 rows of the sub-selection\n",
    "df_sub.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-Means clustering model\n",
    "### Build Model\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 0 1 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "# Define a k-Means clustering model with 2 clusters,\n",
    "# and initialization by k-means++\n",
    "from sklearn.cluster import KMeans\n",
    "km_clus = sklearn.cluster.KMeans(init='k-means++', n_clusters=2)\n",
    "\n",
    "# fit the model on the first 2 columns\n",
    "km_clus.fit(df_sub)\n",
    "\n",
    "# Return the predicted labels of the model\n",
    "km_pred_labels=km_clus.predict(df_sub)\n",
    "\n",
    "# Print the first 10 predicted labels\n",
    "print(km_pred_labels[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot k-Means cluster predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb1fe48e070>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the resulting k-Means predictions on the original scatterplot\n",
    "fig_km, ax_km = plt.subplots(figsize=[10,7])\n",
    "# Set a nicer looking default chart style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Generate the original Scatterplot of Worst Radius and Worst Concavity\n",
    "# this time with colors per cluster by k-Means\n",
    "scat = ax_km.scatter(df_sub['worst radius'], df_sub['worst concavity'], c=km_pred_labels, cmap='plasma', edgecolor='k')\n",
    "\n",
    "# Plot the centroids as a Red X\n",
    "centroids = km_clus.cluster_centers_\n",
    "ax_km.scatter(centroids[:, 0], centroids[:, 1], \n",
    "            marker='x', s=200, linewidths=3,\n",
    "            color='r', zorder=10)\n",
    "\n",
    "# Create a Mesh Grid on the original plot dimensions for finding the \n",
    "# decision boundaries for each cluster (with a minor adjustment of 0.2)\n",
    "x_min, x_max = df_sub.iloc[:, 0].min() - 2, df_sub.iloc[:, 0].max() + 2\n",
    "y_min, y_max = df_sub.iloc[:, 1].min() - 0.2, df_sub.iloc[:, 1].max() + 0.2\n",
    "WRadius_X, Wconcavity_Y = np.meshgrid(np.arange(x_min, x_max, 0.1), \n",
    "                                      np.arange(y_min, y_max, 0.1))\n",
    "# Score k-Means cluster model across the mesh\n",
    "final_grid_km = km_clus.predict(np.c_[WRadius_X.ravel(), Wconcavity_Y.ravel()])\n",
    "\n",
    "# Draw a contour plot to illustrate the model cluster separation\n",
    "final_grid_km = final_grid_km.reshape(WRadius_X.shape)\n",
    "ax_km.contour(WRadius_X, Wconcavity_Y, final_grid_km)\n",
    "\n",
    "ax_km.set_xlabel('Worst Radius', size=13)\n",
    "ax_km.set_ylabel(\"Worst Concavity\", size=13)\n",
    "ax_km.set_title(\"Worst Radius vs Worst Concavity: 2 Clusters by k-Means\\n\"\n",
    "                \"k-Means centroids are marked with a red cross\", size=18)\n",
    "\n",
    "# Add a legend to the chart\n",
    "legend1 = ax_km.legend(*scat.legend_elements(num=1),\n",
    "                    loc=\"upper right\", title=\"Cluster\")\n",
    "ax_km.add_artist(legend1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the k-Means model to ONNX format\n",
    "\n",
    "We will create and add the metadata.json file needed by OML Services to the Zip file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the GMM model to ONNX format. Input data needs to be a numpy format\n",
    "clus_onnx = to_onnx(km_clus, df_sub.to_numpy(), target_opset=12)\n",
    "# Export the model to a file on disk\n",
    "onnxmltools.utils.save_model(clus_onnx, './clus_onnx.onnx')\n",
    "# Create the metadata.json file needed by Oracle Machine Learning Services\n",
    "metadata = {\"function\":\"clustering\",\"clusteringDistanceOutput\":\"scores\"}\n",
    "with open('./metadata.json', mode='w') as f: \n",
    "       json.dump(metadata, f)\n",
    "# Write the final ZIP File to be used in OML Services\n",
    "with ZipFile('./clus_onnx.zip', mode='w') as zf:\n",
    "    zf.write('./metadata.json')\n",
    "    zf.write('./clus_onnx.onnx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Mixture Model (GMM) clustering\n",
    "### Build Model\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prob_of_Cluster0</th>\n",
       "      <th>Prob_of_Cluster1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9990</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.9964</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.9994</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1984</td>\n",
       "      <td>0.8016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.9969</td>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prob_of_Cluster0  Prob_of_Cluster1\n",
       "0            1.0000            0.0000\n",
       "1            1.0000            0.0000\n",
       "2            0.9999            0.0001\n",
       "3            1.0000            0.0000\n",
       "4            0.9990            0.0010\n",
       "5            0.9964            0.0036\n",
       "6            0.9994            0.0006\n",
       "7            0.1984            0.8016\n",
       "8            0.9969            0.0031\n",
       "9            1.0000            0.0000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a Gaussian Mixture Model with 2 clusters\n",
    "from sklearn import cluster, datasets, mixture\n",
    "gmm_clus = mixture.GaussianMixture(n_components=2, covariance_type=\"full\", random_state=1)\n",
    "\n",
    "# Fit the model on the first 2 columns\n",
    "gmm_clus.fit(df_sub)\n",
    "\n",
    "# Return the predicted probabilities of the model\n",
    "gmm_clus_pred=gmm_clus.predict_proba(df_sub)\n",
    "\n",
    "# Print the first 10 sets of predicted probabilities\n",
    "# Probabilities of belonging to Cluster 0 and 1\n",
    "gmm_probs_df = pd.DataFrame(gmm_clus.predict_proba(df_sub), \n",
    "                            columns={'Prob_of_Cluster0','Prob_of_Cluster1'})\n",
    "gmm_probs_df.head(10).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Return the predicted labels of the model\n",
    "gmm_clus_labels=gmm_clus.predict(df_sub)\n",
    "\n",
    "# Print the first 10 predicted labels\n",
    "print(gmm_clus_labels[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot GMM cluster predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb1feb44c40>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the resulting GMM predictions on the original scatterplot\n",
    "# and also see the Elipsoids that represent the Gaussian distributions\n",
    "fig_gmm, ax_gmm = plt.subplots(figsize=[10,7])\n",
    "\n",
    "# Set a nicer looking default chart style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Generate the original Scatterplot of Worst Radius and Worst Concavity\n",
    "# this time with colors per cluster by GMM\n",
    "scat = ax_gmm.scatter(df_sub['worst radius'], df_sub['worst concavity'], \n",
    "                      c=gmm_clus_labels, cmap='plasma', edgecolor='k')\n",
    "ax_gmm.set_xlabel('Worst Radius', size=13)\n",
    "ax_gmm.set_ylabel(\"Worst Concavity\", size=13)\n",
    "ax_gmm.set_title(\"Worst Radius vs Worst Concavity: 2 Clusters by GMM\\n\"\n",
    "                 \"Including the Gaussian Ellipses\", size=18)\n",
    "\n",
    "# Plot the centroids as a Red X\n",
    "gmm_means = gmm_clus.means_\n",
    "ax_gmm.scatter(gmm_means[:, 0], gmm_means[:, 1], \n",
    "            marker='x', s=200, linewidths=3,\n",
    "            color='r', zorder=10)\n",
    "\n",
    "# Create a Mesh Grid on the original plot dimensions for finding the \n",
    "# decision boundaries for each GMM ellipses (with a minor adjustment of 0.2)\n",
    "x_min, x_max = df_sub.iloc[:, 0].min() - 2, df_sub.iloc[:, 0].max() + 2\n",
    "y_min, y_max = df_sub.iloc[:, 1].min() - 0.2, df_sub.iloc[:, 1].max() + 0.2\n",
    "WRadius_X, Wconcavity_Y = np.meshgrid(np.arange(x_min, x_max, 0.1), \n",
    "                                      np.arange(y_min, y_max, 0.1))\n",
    "Grid_Rad_Conc = np.array([WRadius_X.ravel(), Wconcavity_Y.ravel()]).T\n",
    "# Score GMM cluster model across the mesh\n",
    "Final_Grid = gmm_clus.score_samples(Grid_Rad_Conc)\n",
    "# Reshape the mesh to conform with the shape of the original data\n",
    "Final_Grid = Final_Grid.reshape(WRadius_X.shape)\n",
    "# Plot the ellipsis\n",
    "ax_gmm.contour(WRadius_X, Wconcavity_Y, Final_Grid)\n",
    "\n",
    "# Add a legend to the chart\n",
    "leg_gmm = ax_gmm.legend(*scat.legend_elements(num=1),\n",
    "                        loc=\"upper right\", title=\"Cluster\")\n",
    "ax_gmm.add_artist(leg_gmm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMM weights:\n",
      "[0.38740444 0.61259556]\n",
      "\n",
      "GMM Means:\n",
      "[[20.45976511  0.46609291]\n",
      " [13.61907682  0.14956364]]\n",
      "\n",
      "GMM Covariances:\n",
      "[[[2.44852455e+01 1.51286739e-02]\n",
      "  [1.51286739e-02 3.55704453e-02]]\n",
      "\n",
      " [[4.45314937e+00 9.46955958e-02]\n",
      "  [9.46955958e-02 9.61637687e-03]]]\n"
     ]
    }
   ],
   "source": [
    "# Check the weights, means and covariances\n",
    "print('GMM weights:')\n",
    "print(gmm_clus.weights_)\n",
    "print('\\nGMM Means:')\n",
    "print(gmm_clus.means_)\n",
    "print('\\nGMM Covariances:')\n",
    "print(gmm_clus.covariances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the GMM model to ONNX format\n",
    "\n",
    "We will create and add the metadata.json file needed by OML Services to the Zip file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the GMM model to ONNX format. Input data needs to be a numpy format\n",
    "gmm_onnx = to_onnx(gmm_clus, df_sub.to_numpy(), target_opset=12)\n",
    "# Export the model to a file on disk\n",
    "onnxmltools.utils.save_model(gmm_onnx, './gmm_onnx.onnx')\n",
    "# Create the metadata.json file needed by Oracle Machine Learning Services\n",
    "# In this example we will return the Probabilities\n",
    "metadata = {\"function\":\"clustering\",\"clusteringProbOutput\":\"probabilities\"}\n",
    "with open('./metadata.json', mode='w') as f: \n",
    "       json.dump(metadata, f)\n",
    "# Write the final ZIP File to be used in OML Services\n",
    "with ZipFile('./gmm_onnx.zip', mode='w') as zf:\n",
    "    zf.write('./metadata.json')\n",
    "    zf.write('./gmm_onnx.onnx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra useful verification for ONNX information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ir_version: 7\n",
      "producer_name: \"skl2onnx\"\n",
      "producer_version: \"1.10.3\"\n",
      "domain: \"ai.onnx\"\n",
      "model_version: 0\n",
      "doc_string: \"\"\n",
      "graph {\n",
      "  node {\n",
      "    input: \"X\"\n",
      "    output: \"Re_reduced0\"\n",
      "    name: \"Re_ReduceSumSquare\"\n",
      "    op_type: \"ReduceSumSquare\"\n",
      "    attribute {\n",
      "      name: \"axes\"\n",
      "      ints: 1\n",
      "      type: INTS\n",
      "    }\n",
      "    attribute {\n",
      "      name: \"keepdims\"\n",
      "      i: 1\n",
      "      type: INT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Re_reduced0\"\n",
      "    input: \"Mu_Mulcst\"\n",
      "    output: \"Mu_C0\"\n",
      "    name: \"Mu_Mul\"\n",
      "    op_type: \"Mul\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"X\"\n",
      "    input: \"Ge_Gemmcst\"\n",
      "    input: \"Mu_C0\"\n",
      "    output: \"Ge_Y0\"\n",
      "    name: \"Ge_Gemm\"\n",
      "    op_type: \"Gemm\"\n",
      "    attribute {\n",
      "      name: \"alpha\"\n",
      "      f: -2.0\n",
      "      type: FLOAT\n",
      "    }\n",
      "    attribute {\n",
      "      name: \"transB\"\n",
      "      i: 1\n",
      "      type: INT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Re_reduced0\"\n",
      "    input: \"Ge_Y0\"\n",
      "    output: \"Ad_C01\"\n",
      "    name: \"Ad_Add\"\n",
      "    op_type: \"Add\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_Addcst\"\n",
      "    input: \"Ad_C01\"\n",
      "    output: \"Ad_C0\"\n",
      "    name: \"Ad_Add1\"\n",
      "    op_type: \"Add\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C0\"\n",
      "    output: \"label\"\n",
      "    name: \"Ar_ArgMin\"\n",
      "    op_type: \"ArgMin\"\n",
      "    attribute {\n",
      "      name: \"axis\"\n",
      "      i: 1\n",
      "      type: INT\n",
      "    }\n",
      "    attribute {\n",
      "      name: \"keepdims\"\n",
      "      i: 0\n",
      "      type: INT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C0\"\n",
      "    output: \"scores\"\n",
      "    name: \"Sq_Sqrt\"\n",
      "    op_type: \"Sqrt\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  name: \"ONNX(KMeans)\"\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ad_Addcst\"\n",
      "    double_data: 542.9810056466672\n",
      "    double_data: 192.91562136836933\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ge_Gemmcst\"\n",
      "    double_data: 23.297708333333333\n",
      "    double_data: 0.4447381944444444\n",
      "    double_data: 13.887762352941177\n",
      "    double_data: 0.2137245811764706\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 1\n",
      "    data_type: 11\n",
      "    name: \"Mu_Mulcst\"\n",
      "    double_data: 0.0\n",
      "  }\n",
      "  input {\n",
      "    name: \"X\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 11\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "          dim {\n",
      "            dim_value: 2\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  output {\n",
      "    name: \"label\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 7\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  output {\n",
      "    name: \"scores\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 11\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "          dim {\n",
      "            dim_value: 2\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "opset_import {\n",
      "  domain: \"\"\n",
      "  version: 12\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the k-Means ONNX model\n",
    "print(clus_onnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ir_version: 7\n",
      "producer_name: \"skl2onnx\"\n",
      "producer_version: \"1.10.3\"\n",
      "domain: \"ai.onnx\"\n",
      "model_version: 0\n",
      "doc_string: \"\"\n",
      "graph {\n",
      "  node {\n",
      "    input: \"X\"\n",
      "    input: \"Ge_Gemmcst\"\n",
      "    input: \"Ge_Gemmcst1\"\n",
      "    output: \"Ge_Y0\"\n",
      "    name: \"Ge_Gemm\"\n",
      "    op_type: \"Gemm\"\n",
      "    attribute {\n",
      "      name: \"alpha\"\n",
      "      f: 1.0\n",
      "      type: FLOAT\n",
      "    }\n",
      "    attribute {\n",
      "      name: \"beta\"\n",
      "      f: 1.0\n",
      "      type: FLOAT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"X\"\n",
      "    input: \"Ge_Gemmcst2\"\n",
      "    input: \"Ge_Gemmcst3\"\n",
      "    output: \"Ge_Y02\"\n",
      "    name: \"Ge_Gemm1\"\n",
      "    op_type: \"Gemm\"\n",
      "    attribute {\n",
      "      name: \"alpha\"\n",
      "      f: 1.0\n",
      "      type: FLOAT\n",
      "    }\n",
      "    attribute {\n",
      "      name: \"beta\"\n",
      "      f: 1.0\n",
      "      type: FLOAT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ge_Y0\"\n",
      "    output: \"Re_reduced0\"\n",
      "    name: \"Re_ReduceSumSquare\"\n",
      "    op_type: \"ReduceSumSquare\"\n",
      "    attribute {\n",
      "      name: \"axes\"\n",
      "      ints: 1\n",
      "      type: INTS\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ge_Y02\"\n",
      "    output: \"Re_reduced02\"\n",
      "    name: \"Re_ReduceSumSquare1\"\n",
      "    op_type: \"ReduceSumSquare\"\n",
      "    attribute {\n",
      "      name: \"axes\"\n",
      "      ints: 1\n",
      "      type: INTS\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Re_reduced0\"\n",
      "    input: \"Re_reduced02\"\n",
      "    output: \"Co_concat_result0\"\n",
      "    name: \"Co_Concat\"\n",
      "    op_type: \"Concat\"\n",
      "    attribute {\n",
      "      name: \"axis\"\n",
      "      i: 1\n",
      "      type: INT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_Addcst\"\n",
      "    input: \"Co_concat_result0\"\n",
      "    output: \"Ad_C02\"\n",
      "    name: \"Ad_Add\"\n",
      "    op_type: \"Add\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C02\"\n",
      "    input: \"Mu_Mulcst\"\n",
      "    output: \"Mu_C0\"\n",
      "    name: \"Mu_Mul\"\n",
      "    op_type: \"Mul\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Mu_C0\"\n",
      "    input: \"Ad_Addcst1\"\n",
      "    output: \"Ad_C01\"\n",
      "    name: \"Ad_Add1\"\n",
      "    op_type: \"Add\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C01\"\n",
      "    input: \"Ad_Addcst2\"\n",
      "    output: \"Ad_C0\"\n",
      "    name: \"Ad_Add2\"\n",
      "    op_type: \"Add\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C0\"\n",
      "    output: \"Re_reduced03\"\n",
      "    name: \"Re_ReduceLogSumExp\"\n",
      "    op_type: \"ReduceLogSumExp\"\n",
      "    attribute {\n",
      "      name: \"axes\"\n",
      "      ints: 1\n",
      "      type: INTS\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C0\"\n",
      "    output: \"label\"\n",
      "    name: \"Ar_ArgMax\"\n",
      "    op_type: \"ArgMax\"\n",
      "    attribute {\n",
      "      name: \"axis\"\n",
      "      i: 1\n",
      "      type: INT\n",
      "    }\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Ad_C0\"\n",
      "    input: \"Re_reduced03\"\n",
      "    output: \"Su_C0\"\n",
      "    name: \"Su_Sub\"\n",
      "    op_type: \"Sub\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  node {\n",
      "    input: \"Su_C0\"\n",
      "    output: \"probabilities\"\n",
      "    name: \"Ex_Exp\"\n",
      "    op_type: \"Exp\"\n",
      "    domain: \"\"\n",
      "  }\n",
      "  name: \"ONNX(GaussianMixture)\"\n",
      "  initializer {\n",
      "    dims: 1\n",
      "    data_type: 11\n",
      "    name: \"Ad_Addcst\"\n",
      "    double_data: 3.6757541328186907\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ge_Gemmcst\"\n",
      "    double_data: 0.20209137042379102\n",
      "    double_data: -0.0032764897999110537\n",
      "    double_data: 0.0\n",
      "    double_data: 5.3028875797901005\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ge_Gemmcst1\"\n",
      "    double_data: -4.134741968660108\n",
      "    double_data: -2.4046020939980735\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ge_Gemmcst2\"\n",
      "    double_data: 0.47387780564583254\n",
      "    double_data: -0.2438815378302169\n",
      "    double_data: 0.0\n",
      "    double_data: 11.468758469642347\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ge_Gemmcst3\"\n",
      "    double_data: -6.453778236519694\n",
      "    double_data: 1.6061321399049344\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 1\n",
      "    data_type: 11\n",
      "    name: \"Mu_Mulcst\"\n",
      "    double_data: -0.5\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ad_Addcst1\"\n",
      "    double_data: 0.06921614354943251\n",
      "    double_data: 1.692820899234218\n",
      "  }\n",
      "  initializer {\n",
      "    dims: 2\n",
      "    data_type: 11\n",
      "    name: \"Ad_Addcst2\"\n",
      "    double_data: -0.9482860765833241\n",
      "    double_data: -0.4900503263788662\n",
      "  }\n",
      "  input {\n",
      "    name: \"X\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 11\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "          dim {\n",
      "            dim_value: 2\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  output {\n",
      "    name: \"label\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 7\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "          dim {\n",
      "            dim_value: 1\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  output {\n",
      "    name: \"probabilities\"\n",
      "    type {\n",
      "      tensor_type {\n",
      "        elem_type: 11\n",
      "        shape {\n",
      "          dim {\n",
      "          }\n",
      "          dim {\n",
      "            dim_value: 2\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "opset_import {\n",
      "  domain: \"\"\n",
      "  version: 12\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the GMM ONNX model\n",
    "print(gmm_onnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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